Usage
# 环境配置
git clone https://github.com/hiyouga/ChatGLM-Efficient-Tuning.git
conda create -n chatglm_etuning python=3.10
conda activate chatglm_etuning
cd ChatGLM-Efficient-Tuning
pip install -r requirements.txt
# 终端输入
CUDA_VISIBLE_DEVICES=0 python src/infer.py \
--checkpoint_dir path_to_checkpoint # repo files
# PPO训练，创建文件夹path_to_rm_checkpoint，将此repo的文件存入其中，运行下列命令，3090预估50小时
CUDA_VISIBLE_DEVICES=0 python src/train_ppo.py \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 1.0 \
--fp16
# https://chatglm.cn/login_v2?md5=Y2t6Vk81QXFCV09FeE1xQTVSVVM4dz09